business intelligence

Retail analytics is a process that helps to provide crucial data for businesses with regards to inventory, sales, supply chain activities, consumer demand, and more. By implementing an analytics-driven retail environment, organizations can make better decisions for procurement, marketing, merchandising, and other operational considerations. This, in turn, enables retailers to create a better buying experience and for identifying opportunities for organizational improvement.

Businesses around the world are striving every day to become more data-driven, and as such, how they collect and manage this data is evolving. One important topic that has arisen out of this shift is data governance. In this post, we set out to answer the following questions:

Knowledge management is the process where value is derived from knowledge by making it accessible to everyone within an organization. It is a multidisciplinary approach that aims to achieve objectives through the best possible use of knowledge. To learn more about knowledge management, see our recent blog post on the topic.

Humans are naturally visual creatures, so it is no surprise that we search for better ways to portray and understand what data is telling us. Data visualization is one of the most effective ways to achieve this because it enables us to take data or ideas that are abstract and difficult to understand as written, and turn them into something easily understood in a more graphical or visual format. To learn more about data visualization and how organizations use it, see our blog post.

However, for any data visualization to be effective, it requires the right tool – which in many cases can mean a multifunctional tool that includes capacity for both visualization and analysis.

As we become better at collecting and analyzing data, we also have to become better at explaining what it all means. Data visualization is the use of algorithms to create images (graphical and pictorial) from data so that humans can more effectively understand and respond to that data. It is important because it enables us to digest large amounts of complex data that would otherwise be overwhelming or difficult to understand.

To better understand data visualization, there are several questions that we will answer:

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